The granulation of navel orange affects consumers' taste and reduces its quality. It has attracted the attention of fruit farmers and consumers. The detection of navel orange granulation is challenging and has great significance for quality classification. In this paper, the different granulation degrees of Gannan navel oranges are used as the research object to explore the qualitative determination of the granulation degree of Gannan navel oranges by using hyperspectral detection technology. Since the degree of granulation of navel oranges cannot be judged by the naked eye, the samples of navel oranges are marked with serial numbers, and then the spectrum is measured. Finally, the samples were cut to determine the degree of granulation. According to the degree of granulation, it is classified as non-granulation (the granulation area is 0%); light granulation (granulation area less than 25%); and medium granulation (granulation area 25%~50%). Take 3 points uniformly at the bottom of these three types of navel oranges, each with 174 samples, and a total of 522 sample data are used as the rows for constructing the spectral matrix. The near-infrared hyperspectral imaging system was used to collect the hyperspectral image information of the sample in the 397.5~1 014 nm band and then use the ENVI 4.5 software was used to extract the average spectral information the sample by selecting the Region of Interest (ROI). Three dimensionality reduction methods: Principal Component Analysis (PCA), Successive Projections Algorithm (SPA), and Uninformative Variable Elimination (UVE) are used to reduce the dimensionality of the spectral data to eliminate irrelevant variables and extract useful information. The original spectrum has 176 wavelengths. PCA selects 6 principal component factors. SPA selects 17 characteristic wavelengths, and UVE selects 54 characteristic wavelengths. The full spectrum data and the variables selected by the three-dimensionality reduction methods are used as input to establish Partial Least Squares Discriminant Analysis (PLS-DA) and Least Squares Support Vector Machines (LS-SVM) model. In the established PLS-DA modeling method, the highest false positive rate of PCA-PLS-DA is 25.58%, and the lowest false-positive rate of UVE-PLS-DA is 5.38%. The LS-SVM modeling method is based on the two kernel functions of RBF-Kernel and LIN-Kernel, and the effect of RBF-Kernel modeling is better than that of LIN-Kernel generally. And the model established after UVE wavelength screening is better than other dimensionality reduction methods, which reduces the model's false positive rate. The UVE-LS-SVM model based on RBF-Kernel has the best effect and the highest detection accuracy, and the total misjudgment rate of classification is 0.78%, achieves the best results. This study shows that the established model can distinguish navel oranges with different granulation degrees. The model reduces the spectral dimension while also reducing the misjudgment rate with only 30.68% of the data, which is useful for promoting the quality of the navel orange industry with certain practical significance.